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Humanoid Robot Actuators

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Re: Humanoid Robot Actuators

#82
post #12

Earlier quoted context omitted.

Their approach required pre-computation and simulation before execution. If you watch their videos carefully, you can see the advance planning work on some of the screens.

Some of it was pre-computed. The middle layer, if you like. The Boston Dynamics group had walking gait of a sort nailed in the 80's; the trotting-on-the-spot that BigDog did was essentially a continuation of those mechanics and that's all based on a conceptually simple balance problem which is intrinsically reactive and not pre-planned. So that's what was going on at the lowest level. At the top level you have the ac…

Right. Early thinking on this was zero moment point (ZMP) control. The zero moment point is where, if you land from a stride there, you continue going in the same direction at the same speed. You can speed up by displacing the landing point backwards a bit, and slow down by displacing it further, and turn by displacing it left or right.

When a foot or feet are in contact with the ground, the goal is to stabilize the posture. When in flight, the goal is to hit the zero moment point. Reactive controllers do that. The planner picks the next landing point.

ZMP robots tend to start moving by running in place, then slowly transition to fast forward motion. You see a lot of robot videos like that.

The next step up is to control takeoff. Bend knees, fall forward, launch. Launch angle is a planning problem. Speed and direction changes become much faster. Basic gymnastics are almost within reach. The planner controls launch angle and landing point, and the lower level reactive controls do the millisecond-level control.

It's possible to do all that from first principles of classical dynamics. That level of performance was reached before machine learning. That's what you're looking at in older BD videos.

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